With roots beyond behavioural economics to psychology, nudges can be applied for influencing healthy behaviours such as food choice and portions to decrease obesity for better public health outcomes. However, the effectiveness of the type of nudges are contentious with conflicting literature. In this pilot study, we conducted a 23-day study surveying the food choices that included portion, locus of control, demographic data, and psychological measures of personality, perceived stress, narcissism, regulatory focus, food choice motive and dietary restraint, with the participants given four intervention conditions of 12 instant messaging sent every two days through WhatsApp. The messages were either factual (control), focused on consequences, through social comparison, or persuasive. Running over the COVID19 pandemic, 17 participants completed the full surveys showing significant effects between the experimental conditions with the psychological parameters except for diet confidence and extraversion and conscientiousness, as well as cognitive restraint. We found BMI and waistline measurements to be suitable measurements, with promising results from the fear and social comparison nudges for food-related behaviours and exercise. Our pilot findings have implications to the use of nudges upon which future studies investigating psychological factors can build on.

Korea Atomic Energy Research Institute (KAERI) established a multi-dimensional hydrogen analysis system to evaluate a hydrogen release, distribution, and combustion in the containment of a nuclear power plant using MAAP, GASFLOW, and COM3D. KAERI developed the COM3D analysis methodology on the basis of the COM3D validation results against the experiments of ENACCEF and THAI. The proposed analysis methodology accurately predicts the peak overpressure with an error range of approximately ±10% using the Kawanabe turbulent flame speed model. KAERI performed a hydrogen flame acceleration analysis using the multi-dimensional hydrogen analysis system for a severe accident initiated by a station blackout (SBO) under the assumption of 100% metal-water reaction in the reactor pressure vessel for evaluating an overpressure buildup in the Advanced Power Reactor 1400 MWe (APR1400). The COM3D calculation results using the established analysis methodology showed that the calculated peak pressure in the containment was much lower than the fracture pressure of the APR1400 containment. This calculation result may have resulted from a large air volume of the containment, a reduced hydrogen concentration owing to passive auto-catalytic recombiners installed in the containment, and a lot of stem presence during the hydrogen flame acceleration in the containment. Therefore, we can know that the current design of the APR1400 containment maintains its integrity when the flame acceleration occurs during the severe accident initiated by the SBO accident.

Singing melody extraction is the task that identifies the melody pitch contour of singing voice from polyphonic music. Most of the traditional melody extraction algorithms are based on calculating salient pitch candidates or separating the melody source from the mixture. Recently, classification-based approach based on deep learning has drawn much attentions. In this paper, we present a classification-based singing melody extraction model using deep convolutional neural networks. The proposed model consists of a singing pitch extractor (SPE) and a singing voice activity detector (SVAD). The SPE is trained to predict a high-resolution pitch label of singing voice from a short segment of spectrogram. This allows the model to predict highly continuous curves. The melody contour is smoothed further by post-processing the output of the melody extractor. The SVAD is trained to determine if a long segment of mel-spectrogram contains a singing voice. This often produces voice false alarm errors around the boundary of singing segments. We reduced them by exploiting the output of the SPE. Finally, we evaluate the proposed melody extraction model on several public datasets. The results show that the proposed model is comparable to state-of-the-art algorithms.

Balancing the levels of ammonia, in poultry facilities plays a role in enhancing the welfare and productivity of animals. Various environmental factors such as temperature, humidity, ammonia concentrations, carbon dioxide concentration and exposure to light have an impact on the wellbeing and efficiency of poultry. It is essential to monitor these elements to achieve outcomes necessitating practical, reliable and cost-effective solutions. Leveraging the Internet of Things (IoT) in conjunction with LoRa-WAN (Long Range Wide Area Network) technology presents an opportunity in the industry by enhancing connectivity among devices and facilitating real time data transmission. This research introduces a hardware and software setup for monitoring conditions at poultry farms through LoRa WAN and IoT technologies. More-over researchers employed Random Forest Machine Learning prediction models to tune the concentration of Ammonia (NH3) in poultry facilities across a wide area or multiple Poultry houses. Comparative assessments were conducted to evaluate the proposed system against devices revealing a correlation exceeding 0.9830 between the sensors and standard equipment while costing 28% less than traditional tools. The prototype displayed outcomes underscoring its potential, for managing poultry facilities across multiple farms with centralized oversight.

Overexpression of BRF2, a selective subunit of RNA polymerase III, has been shown to be crucial in the development of several types of cancers, including breast cancer and lung squamous cell carcinoma . Predominately, BRF2 acts as a central redox-sensing transcription factor (TF) and is involved in rescuing oxidative stress (OS) -induced apoptosis. Here, we showed a novel link between BRF2 and DNA damage response. Due to the lack of BRF2 specific inhibitors, through virtual and molecular dynamics screening, we identified potential drug candidates that interfere with BRF2-TATA-binding Protein (TBP)-DNA complex interactions based on binding energy, intermolecular, and torsional energy parameters. We experimentally tested Bexarotene as a potential BRF2 inhibitor. We found that Bexarotene (Bex) treatment resulted in a dramatic decline in oxidative stress (Tert-butylhydroquinone (tBHQ))-induced levels of BRF2 and consequently, lead to a decrease in cellular proliferation of cancer cells which may in part be due to drug pretreatment induced reduction of ROS generated by the oxidizing agent. Our data thus, provide the first experimental evidence that BRF2 is a novel player in DNA damage response pathway and Bexarotene can be used as a potential inhibitor to treat cancers with the specific elevation of oxidative stress.

This study delves into the essential characteristics of the Metaverse and its impact on the real world. The Metaverse, a parallel universe composed of countless 3D virtual worlds, has become a hot topic in technology. With the development of advanced technologies such as Virtual Reality (VR), Augmented Reality (AR), blockchain, and Artificial Intelligence, the Metaverse is gradually changing people's lifestyles and work methods, expanding the boundaries of the digital economy. This study profoundly investigates the attributes of the Metaverse, such as asset digitization, application scenario-based, scope economy, industry universality, integrated innovation, and the platform ecosystem. Additionally, we propose a theoretical framework to aid in understanding and shaping this emerging field. We conducted a thorough study and analysis of a specific case, explored the key features of the Metaverse, unveiled the profound impact of Metaverse characteristics on social and economic development, and verified our theoretical framework with this case. This research provides new perspectives and methods for academic studies and offers robust support and guidance for practical applications and decision-making.

With the widespread Application of Virtual Reality (VR) in education, optimizing foreign language learning in VR has become a focal point of research. This paper introduces a comprehensive theoretical framework (VR-CCL) based on constructivism and cognitive load theory to enhance foreign language learning in VR. Through a literature review, we explore the applications of VR in education, foreign language learning theories, and prior works on technology-assisted language learning. We further detail the three main components of the VR-CCL framework and validate its effectiveness through two case studies: Duolingo VR and Rosetta Stone VR. Finally, we discuss the strengths and limitations of the framework and its implications for educators and developers.

This study was conducted to investigate the prevalence of Coxiella burnetii infection according to cattle breeds and growth types. A total of 491 cattle [cattle breed: 216 dairy cattle and 275 beef cattle; according to growth type: indoor housing (n = 294) and grazing (n = 197)] were tested for the presence of C. burnetii DNA and antibodies against C. burnetii using a commercial enzyme-linked immunosorbent assay (ELISA). Overall, 10.8% and 8.8% of the cattle were positive by PCR and for C. burnetii antibodies, respectively. The prevalence of C. burnetii was significantly higher in beef cattle than in dairy cattle using PCR (13.6% vs 7.4%; P = 0.032) and ELISA (14.6% vs 1.4%; P = 0.000), respectively. The overall infection rate of C. burnetii was significantly high in grazing cattle (PCR: 24.9%, ELSIA: 21.3%; P = 0.000) compared with housing cattle (PCR: 1.4%, ELISA: 0.3%). The results indicate that beef cattle have a significantly higher risk of contracting C. burnetii infection compared with dairy cattle (21.5% vs. 7.9%, χ2 = 5.82, P = 0.000, odds ratio = 3.197, 95% CI: 1.80-5.67). In addition, the infection of C. burnetii was significantly associated with grazing (P = 0.000). Moreover, a risk of contracting C. burnetii infection in grazing cattle was increased by 32.57-fold (95% CI: 12.84-82.60, P = 0.000) compared with indoor housed cattle. The phylogenetic analysis based on the IS111 gene revealed that our isolates were grouped together with humans, ticks, goats, and cattle isolates found in several countries. C. burnetii isolates circulating in the Republic of Korea exhibit genetic variations. Consequently, our results suggest that cattle are potential reservoirs for C. burnetii infection and most importantly, grazing acts as a high risk factor for the occurrence and transmission of this infection.

Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) are comorbidities that result from the sharing of common genes. The molecular background of comorbidities can provide clues for the development of treatment and management strategies. Here, the common genes involved in the development of the two diseases and in memory and cognitive function are reviewed. Network clustering based on protein-protein interaction networks identified tightly connected gene clusters that have an impact on memory and cognition among the comorbidity genes of AD and T2DM. Genes with functional implications were intensively reviewed, and relevant evidence was summarized. Gene information will be useful in the discovery of biomarkers and the identification of therapeutic targets for AD.

Thyroid hormones have a catabolic effect on bone homeostasis. Hence, this study aimed to evaluate serum vitamin D, calcium, and phosphate and bone marker levels and bone mineral density (BMD) among patients with different thyroid diseases. This cross-sectional study included patients with underlying thyroid diseases (n=64, hyperthyroid; n=53 euthyroid; and n=18, hypothyroid) and healthy controls (n=64). BMD was assessed using z-score and left hip and lumbar bone density (g/cm2). Results showed that the mean serum vitamin D Levels of all groups was low (<50 nmol/L). Thyroid patients had higher serum vitamin D levels than healthy controls. All groups had normal serum calcium and phosphate levels. The bone marker levels were significantly high in the hyperthyroid group and low in the hypothyroid group. The z-score for hip and spine did not significantly differ between thyroid patients and control groups. The hip bone density was remarkably low in the hyperthyroid group. In all groups, the serum vitamin D levels were below the cutoff value. However, thyroid patients had a higher serum vitamin D level than healthy controls. The carboxy terminal collagen crosslink and procollagen type I N-terminal propeptide levels were high in hyperthyroid patients and low in hypothyroid patients. Further, hyperthyroid patients had a low hip BMD.

In this paper, we have proposed an effective metasurface design to accomplish cloaking of equilateral patch antennas and their array configuration. As such, we have exploited the concept of electromagnetic invisibility, employing the mantle cloaking technique with the intention to eliminate the destructive interference ensuing between two distinct triangular patches situated in a very congested arrangement (sub-wavelength separation is maintained between the patch elements). Basically, we demonstrate that implementation of the planar coated metasurface cloaks onto the patch antenna surfaces compels them to become invisible to each other, at the intended frequencies. In effect, an individual antenna element does not sense the presence of the other, in spite of being in a rather close vicinity. We also exhibit that the cloaks successfully reinstate the radiation attributes of each antenna in such a way that it emulates their respective performance in an isolated environment. Moreover, we have extended the cloak design to an interleaved one dimensional array of the two patch antennas and it is shown that the coated metasurfaces assure efficient performance of each array in terms of their matching as well as radiation characteristics, in turn, enabling them to radiate independently for various beam-scanning angles.

We still do not have satisfactory answers about the inflationary and accelerating expansions of the universe, the missing mass problem, and the origins of the four fundamental forces and the quantum phenomena. Here, we introduce a new model of matter and space and a postulate on a constant of motion, and then show that an equation of motion derived from the constant of motion could provide new understandings of the above problems. For example, we show that there is a natural inflation scenario and that the co-moving distance to the closest accelerating supernova is clearly given as $c/(\sqrt{3} H)$ ($\sim$8.1 billion ly). In addition, we show that, in a system including a proton and an electron, the equation of motion has eight acceleration terms, some of which are suspected to be responsible for the four fundamental forces and the galaxy rotation anomaly. From further discussion, we conjecture that in order to understand the gravitational and strong interactions and the quantum phenomena, it is necessary to consider the expansion of the universe. The lensing mass distribution is explained, based on another postulate on a method of representing superposition.

Electromagnetic cloaking approach is employed with the intention to curb the destructive effects of mutual interference for rectangular and circularly shaped patch antennas situated in a tight spacing (sub-wavelength separation is employed). Primarily, we show that by coating the top surface of each patch with an appropriately designed metasurface, it is possible to considerably reduce the mutual coupling between the said antennas. We also demonstrate that, despite the close proximity of these patches, the individual radiation characteristic of each antenna is reinstated to emulate their performance in an isolated scenario. Furthermore, the cloak construct is extended to a tightly spaced, interleaved linear patch antenna array configuration and it is shown that the coated metasurfaces successfully enhance the performance of each array in terms of their matching characteristics, total efficiencies as well as their far-field realized gain patterns for a broad range of beam-scan angles. The decoupling and cloaking action of these metasurfaces are demonstrated by various full-wave numerical simulation results. We believe that the simple planar structure of our metasurface design (according to simulation models) makes it feasible for fabrication and the fact that it is used to accomplish cloaking of an electrically large antenna surface, makes it unique in itself.

Vein blood sampling is one of the methods of mass blood sampling, and is an act of drawing blood from a vein for blood type discrimination, confirmation of various physiological indicators, disease diagnosis, and the like, and is the most commonly used blood sampling method. An important point in vein blood sampling is to search for the exact location of the vein and insert the blood sampling. This is influenced by the patient's obesity, skin and blood vessel conditions, and the experience of the clinical technologist, nurse, and resident who performs the blood sampling. It is required to perform blood sampling technique practice. However, due to many limitations of the practice room or laboratory, there is a problem of using only a limited environment and model for clinical practice. As a result, many medical educational institutions have situations in which only fragmentary clinical practices are performed, and it is difficult to per-form a large number of blood sampling skills practices, so they do not provide enough experience to understand the actual skill field. In this paper, we propose a method for developing a virtual reality-based vein blood sampling simulator to practice a large number of blood sampling techniques without restrictions. The proposed vein blood sampling simulator can operate a 3D model related to vein blood sampling using HMD Controller and Haptic in a virtual space for vein blood sampling practice by wearing an HMD (Head mounted display), and can perform vein blood sampling practice through interaction with the patient 3D model. there is. In addition, the effectiveness of the simulator developed for dental students was verified, and as a result of the verification, the potential of the proposed vein blood sampling simulator was confirmed.

This study introduces the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF), an innovative machine learning-based approach to personalize educational pathways. DFDLOF leverages advanced algorithms for in-depth analysis of learner data, enabling the dynamic customization of educational content and methodologies. Its application in diverse online platforms, including Khan Academy and Coursera, underscores its adaptability and scalability. DFDLOF enhances learner engagement and outcomes by providing tailored learning experiences and real-time feedback. Key insights reveal its strengths in personalized education, particularly in dynamically adapting learning paths and the importance of real-time feedback. Challenges identified include integration complexities and data privacy concerns. Future research directions emphasize the need for longitudinal impact studies and broader demographic applications. This study contributes significantly to the evolving domain of adaptive learning technologies, demonstrating the transformative potential of machine learning in personalizing education.

Heat stress due to climate warming can significantly affect the synthesis of sex hormones in male adolescents, which can impair the ability of the hypothalamus to secrete gonadotropin-releasing hormone on the hypothalamic-pituitary-gonadal axis, which leads to a decrease in luteinizing hormone and follicle-stimulating hormone, which ultimately negatively affects spermatogenesis and testosterone synthesis. For optimal spermatogenesis, the testicular temperature should be 2–6°C lower than body temperature. Heat stress directly affects the testes, damaging them and re-ducing testosterone synthesis. Additionally, chronic heat stress abnormally increases the level of aromatase in Leydig cells, which increases estradiol synthesis while decreasing testosterone, lead-ing to an imbalance of sex hormones and spermatogenesis failure. Low levels of testosterone in male adolescents lead to delayed puberty and incomplete sexual maturation, negatively affect height growth and bone mineral density, and can lead to a decrease in lean body mass and an in-crease in fat mass. In order for male adolescents to acquire healthy reproductive capacity, it is recommended to provide sufficient nutrition and energy, avoid exposure to heat stress, and pro-vide foods and supplements to prevent or repair testosterone reduction, germ cell damage, and sperm count reduction caused by heat stress so that they can enter a healthy adulthood.

In order to sustain the marine environment relationship between the environmental factors and marine biota must be investigated. This study aims to study such a correlation in the adjacent waters of Nan Wan Bay, Kenting, Taiwan, and to explore the covariance of environmental factors on marine life (whether they enhance or diminish it). To achieve these objectives, factor analysis was used to identify potential latent variables that could affect the water quality of the adjacent waters, including phytoplankton (quantity), zooplankton (quantity), and fish (quantity and species). These potential variables were named based on the results of past studies and related literature. Finally, a structural equation model (SEM) was constructed to establish a marine ecological model among the potential variables. The factor analysis results revealed that nutrients, upwelling, and primary productivity are the primary factors affecting the environmental changes in the adjacent waters of Nan Wan Bay. The comprehensive SEM showed that nutrients and primary productivity significantly impact plankton. Path analysis indicated that primary productivity has the highest direct effect on the phytoplankton cluster. However, the upwelling has a low significance in its effect on phytoplankton, zooplankton, and fish clusters. Compared to the results of past related studies, the SEM proposed in this study can reflect a considerable degree of change in the waters.

This study introduces the Data-Driven Personalized Learning Model (DDPLM), a sophisticated framework designed to enhance foreign language acquisition through the integration of big data analytics. Implemented within the educational platforms Edmodo and Duolingo, DDPLM utilizes real-time data processing to tailor learning paths and content dynamically to individual learner needs. Our findings indicate significant improvements in language learning efficiency, engagement, and retention. The model's adaptability across different digital environments showcases its potential scalability and effectiveness in various educational contexts. Additionally, the research addresses the critical role of personalized feedback and adaptive challenges in maintaining learner motivation and promoting deeper linguistic comprehension. The outcomes suggest that DDPLM significantly transforms traditional language education, making it more personalized, efficient, and aligned with individual learning preferences.

With the rapid spread of carbon trading in the global economy, the interactions of prices between carbon (or clean/renewable energy) and traditional fossil energies such as coal and oil have raised growing attention, but little research have discussed their dynamic volatility spillover and time-varying correlation. The purpose of this study is to investigate these issues, for the weekly data of EUA futures, Biofuel and Brent oil prices from 25 October 2009 to 5 July 2020. We employ the VAR-GARCH model with the BEKK specification. Our results are summarized as follows. At first, we identified the sudden changes and the volatility persistence in the three markets, and also confirmed that the volatility of the markets has changed significantly over time. Secondly, we find that there are a weak volatility spillover effect among the three markets, while a strong spillover effect between the EUA and Brent oil markets. Lastly, in financial markets, the EUA can be used as a hedging portfolio for the Biofuel and Brent oil markets. These results can help investors to well compose their portfolios and manage their investment risks, and help potential pollutant emission sources to join in carbon market in a cost-effective way.

Host immunological pathways have been proposed including TH1, TH2, TH3, TH9, TH17, TH22, TH1-like, and THαβ immune reactions. Besides TH2 and TH9 immune responses which are against multicellular parasites, host immunological pathways against viruses, intracellular micro-organisms (bacteria, protozoa, and fungi), and extracellular micro-organisms can use programmed cell death pathways to trigger immune reactions or to execute effective strategies to eliminate these pathogens. These programmed cell deaths include apoptosis, autophagic death, pyroptosis, ferroptosis, necroptosis, and NETosis. Apoptosis is related to host anti-virus eradicable THαβ immunity. Autophagic death is related to host anti-virus tolerable TH3 immunity. Pyroptosis is related to host anti-intracellular micro-organism eradicable TH1 immunity. Ferroptosis is related to host anti-intracellular micro-organism tolerable TH1-like immunity. Necroptosis is related to host anti-extracellular micro-organism eradicable TH22 immunity. NETosis is related to host anti-extracellular micro-organism tolerable TH17 immunity.

Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of iot data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan 'an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase of sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control.

A new type of gold nanoparticles was synthesized by using benzoyl pyrazolone as a capping agent. The synthesized AuNPs exhibited a spherical shape and a monodisperse and fluorescence emission characteristic peak at 650 nm. The AuNPs –BMPBP was demonstrated as sensitive and selective fluorescent chemosensor for detection of Al ³⁺ ion. In the presence of Al ³⁺ ion, the fluorescent emission of BMPBP-AuNPs at 650 nm, increased with an increasing concentration of Al ³⁺ ion with a low detection limit (2 µM), the proposed method provided to apply determination of Al ³⁺ ion of tap water with satisfactory results.

Resection of malignant lesions in the oral and maxillofacial area causes functional and morphological defects. To recover from these defects, reconstruction surgery is needed such as a primary closure or a flap. There are advantages and disadvantages to the method of using the primary closure and the flap, and the choice of procedure is entirely up to a surgeon. The purpose of this study is to evaluate availability of primary closure in patients. For 10 years from January 2010 to May 2020, patients who recovered using flaps after removing malignant lesions from the oral cavity and those who closed the lesion using primary closure were investigated. The investigation was conducted by searching the database of the hospital. There were 85 patients who removed malignant lesions in the oral and maxillofacial area, and among them 16 patients closed the soft tissue defects using primary closure. Operation time, histologic types, stages, wound status, and period of hospitalization according to the patients' medical records were investigated retrospectively. Between the staging and the primary closure, statistically significant differences were observed between stage I and stage II patients in 13 patients using the primary closure. (P=0.046). Statistically significant differences were identified between operation time and primary closure, with an average of 4.02 hours (P=0.015). The average period of hospitality for patients who underwent primary closure was 13.4 days, compared to 26.7 days for those who underwent flap surgery. This difference was statistically significant (P=0.0003). This study evaluated the use of primary closure in patients who underwent resection of malignant lesions in the oral and maxillofacial area. Although the majority of patients underwent reconstruction with flaps, our results showed that primary closure can be a viable option for certain patients with early-stage lesions. Therefore, oral and maxillofacial surgeons should carefully consider various evaluation criteria and select appropriate reconstruction methods to optimize functional and aesthetic outcomes for each patient.

This contribution addresses dry sliding friction of a “Non-Asbestos Organic” friction material (pad) rubbed against a pearlitic grey cast iron disc. This configuration is commonly used in brakes in US, Europe and Asia. The initial stage of friction is of particular interest when addressing “creep-groan” phenomena occurring in passenger and sport utility vehicles with automatic transmissions. The custom built Universal Friction Tester allowing the variation of sliding speed, test temperature, normal load, stiffness and humidity of the system was used in this experiment in combination with surface analysis by polarized light microscopy, scanning electron microscopy and stylus profilometry. The obtained results indicate that the “stick” phase in the so-called “stick-slip” phenomenon does not really exist. The surfaces of pad and disc are in continuous relative movement. The complexity of surfaces does not allow for a definitive description of exact mechanisms responsible for the detected changes of friction forces. The observed friction process can be rather described as a stretching with possible localized relaxations causing the jerky behavior in the stretching phase, followed by slip between two materials in contact. Understanding of factors contributing to the stretching at different scale levels of friction system is necessary for development of proper models. An accurate friction model should also incorporate the vibrational element introduced by phenomena occurring at the friction surfaces. The absence of heterogeneous regions on the friction surface and “evenly distributed friction level” can help when mitigating creep-groan in the investigated brake system. Understanding of factors on different scales of friction is also required when developing the improved brakes.

Regulatory T cells are key immune cells to initiate tolerable immune responses. TGFβ is the key cytokine of regulatory T cells. TGFβ is also the master cytokine to stimulate tissue fibrosis. Systemic sclerosis is an autoimmune disease related to overt organ fibrosis. Thus, systemic sclerosis is a disease related to regulatory T cell over-representation. Besides, many end stage organ failures including heart failure, liver cirrhosis, uremia, and pulmonary fibrosis also related to tissue fibrosis. Thus, TGFβ producing regulatory T cells are also involved in the pathogenesis of these end stage organ failure. Once we know the significance of TGFβ and it induced tolerable immune reactions in the pathophysiology of end organ failure, we can develop better prevention or therapeutic strategies to combat these detrimental disorders.

Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.

In this paper, a positive answer to the open question of Fabino et al. is given, and the concept of I\c sik type set valued contractions is introduced and fixed point theorem for such contractions is established. Examples to support main theorems are given, and an application to integral inclusion is given.

As a procedure deriving UH (unit hydrograph), the root selection method necessitates only storm runoff data. However, this method must deal with the uncertainty related to the noise fluctuation of runoff ordinates and derive one optimal UH from many storms. This study proposes a procedure that applies the Savitzky-Golay filter to smooth the noise fluctuation of the runoff ordinates and uses the linear combination of UHs from individual storms to derive an optimal UH. The proposed method is applied to the storms of the Nenagh River basin in Ireland. The applicability of the Savitzky-Golay filter for smoothing the noise fluctuation of storm runoffs is examined by means of the Nash-Sutcliffe efficiency index. Furthermore, the root selection method is extended to also estimate IUHs. The results show that the adoption of the Savitzky-Golay filter improves the applicability of the root selection method and that the optimal UH predicts accurately the time-to-peak and peak discharge.

This study generalized the best copula to characterize the joint probability distribution between rainfall severity and duration in Peninsular Malaysia using two dimensional copulas. Specifically, to construct copulas, Inference Function for Margins (IFM) and Canonical Maximum Likelihood (CML) methods were specially exploited. For the purpose of achieving copula fitting, the derived rainfall variables by making use of the Standardized Precipitation Index (SPI) were fitted into several distributions. Five copulas, namely Gaussian, Clayton, Frank, Joe and Gumbel were put to the tests to establish the best data fitted copula. The tests produced acknowledged and satisfactory results of copula fitting for rainfall severity and duration. Surveying the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), only three copulas produced a better fit for parametric and semi parametric approaches. Finally, two consistency tests were conducted and the results had shown that Frank Copula produced consistent results.

The mechanism of immune activation and de-activation is very important. Host should trigger effective immunity against pathogen infection. And, host cannot trigger immunity against self-antigens that will lead to detrimental autoimmune disorders. Spleen and liver have opposite roles in mediating immune responses. Spleen is the organ involving in immune activation, and liver is the organ involving in immune de-activation. Sympathetic nerve system and parasympathetic nerve system have opposite roles in mediating immune responses. Sympathetic nerve system is involving in pro-inflammatory, and parasympathetic nerve system is involving in anti-inflammatory. ACTH and glucocorticosteroid have opposite roles in mediating immune responses. ACTH is involving in activating adaptive immunity with inhibiting innate immunity, and glucocorticosteroid is involving in activating innate immunity with inhibiting adaptive immunity. The molecular chaperons, heat shock proteins, which are induced by fever, have the vital roles in immune activation. On the contrary, IgD B cells and gamma-delta T cells are causing immune de-activation via the mechanism of clonal anergy. These mechanism can help us the know the work of immunological pathways better for better control of infectious diseases and autoimmune disorders.

Changes in crude oil price affect the shipping freight market in three different channels. This study explores the dependence structure between oil prices and maritime freight rates to identify the strongest channel. Therefore, it investigates the relationship between oil prices and three major maritime freight rates; the Baltic Dry Index (BDI), the Baltic Dirty Tanker Index (BDTI), and the Baltic Clean Tanker Index (BCTI). We employ the decomposition method, not studied in the existing literature. The copula approach identifies the time-varying effects and asymmetry in the tail dependence structure between oil prices and freight rates. The main results of this analysis are as follows. The decomposed components display different conditional dependence patterns, and asymmetry is revealed in the upper and lower tail dependence. In the long run, we find more dependence in extreme periods like the financial crises. In short-run fluctuations, we find the dependence increases in an economic boom. The implications of the results suggest that dependence can vary over time and may change depending on extreme events, implying that the complementary strategies of the long run and short run should be different.

This narrative review provides a thorough examination of medication-related osteonecrosis of the jaw (MRONJ), encompassing its etiology, the role of drug holidays, and contemporary treatment approaches. By systematically analyzing literature from 2022 to 2023, this paper synthesizes the latest findings and trends in MRONJ research. The study emphasizes the importance of a holistic understanding of MRONJ, beginning with its etiological factors, assessing the therapeutic implications of drug holidays, and evaluating the efficacy of current treatment modalities. This review aims to offer clinicians and researchers a consolidated resource, highlighting the intricacies of MRONJ management and proposing directions for future research. Through this comprehensive approach, the paper seeks to advance the understanding and treatment of MRONJ, ensuring an evidence-based, patient-centric approach in clinical practice.

The purpose of this study is to explore the effects of dual-task training, including cognitive tasks, on cognitive and bodily functioning and β-amyloid levels in Alzheimer's dementia patients. The subjects were 34 inpatients diagnosed with Alzheimer's dementia at a nursing hospital located in Gyeongsansi, South Korea. The patients were randomly divided into a dual-task group (n = 16) and a single-task group (n = 18). The dual-task group performed cognitive tasks at the same time as exercising tasks, while the single-task group performed only exercise tasks. Each group was trained for 30 minutes three times a week for eight weeks. The Mini-Mental State Examination was used to measure the patients’ cognitive function. Static and dynamic balance were measured to evaluate bodily functioning. Static balance was measured using Biorescue, while dynamic balance was measured using the Berg Balance Scale. Blood analysis was performed to measure levels of β-amyloid, which is known to cause Alzheimer's dementia. Both groups exhibited statistically significant improvements in gait function after the training (p < .05). The dual-task group exhibited statistically significant differences in cognitive function, static and dynamic balance function, and β-amyloid levels after training (p < .05). A significant difference was observed between the two groups (p < .05). Dual-task activities were found to be effective in improving cognitive and bodily functioning and reducing β-amyloid levels in Alzheimer's dementia patients. Therefore, dual-task training is thought to be an effective method of treating and preventing Alzheimer's dementia.

Today’s intruders usually send attacking commands to a target system through several stepping-stone hosts, in order to reduce the chance of being detected. With stepping-stone intrusion (SSI), the intruder’s identity is hidden behind a long interactive chain of hosts and very hard to detect. An effective approach for SSI detection (SSID) is to estimate the length of the chain. This type of method is called network-based SSID. Most existing network-based SSID worked effectively only when intruders’ session manipulation was not present. These known SSID algorithms are either weak to resist intruders’ chaff-perturbation manipulation or having very limited capability in resisting attacker’s session manipulation. This paper develops a novel network-based SSID algorithm resistant to intruders’ chaff-perturbation by using packet crossover. Our proposed SSID algorithm is simple and easy to implement as the number of packet crossovers can be easily computed. We conduct rigorous technical proofs to verify the correctness of our proposed algorithm. The experimental results show that our proposed SSID algorithm works effectively and perfectly in resisting intruders’ chaff-perturbation up to 50% chaff rate.

Various studies on the physical characteristics of the canopy gaps in tropical and subtropical regions but that in the mangrove swamp forest in Malaysia is little known. The objectives of this study are to assess the canopy gaps physical characteristics (size, form, direction), to assess the changes in soil physical characteristics and microenvionment in and around the canopy gaps. Five canopy gaps were selected formed by lightning strikes, gaps size and shapes were determined using the portable Global Positioning System and photographic images were analysed using ForestCrown. Result shows that canopy gap opening has increased the total light reaching the forest floor, changed the canopy gap microenvironment. Strong correlation between canopy opening percentage and temperature of gaps explains the changes of canopy gaps characteristics. These changes have contributed to new seedling germination, regeneration and maintaining the canopy gap dynamics.

Powdery mildew disease, caused by Sphaerotheca fusca, is a major disease affecting cucumbers cultivated in greenhouses. This study was conducted to find defense genes induced by DL-3-amino-butyric acid (BABA) and powdery mildew in cucumber. The 2000 and 5000 mg/L BABA treated cucumber exhibited disease severity of 25% and 5%, respectively, whereas the water treated cucumber leaves were severely infected, with a disease severity of 90%. BABA did not effect spore germination of the powdery mildew pathogen,. showing BABA is not antifungal agent against the pathogen. In quantitative real-time PCR analysis, BABA treated cucumber upregulated transcriptional level of the defense genes CsPAL, CsPR3,CsPR1, CsLOX1, CsLOX23, Cs LecRK6.1, CsWRKY20, and Cupi4 in cucumber to maximum levels at 48 h, whereas CsLecRK6.1 reached maximum expression after 24 h and futher salicylic acid (SA) levels were significantly increased in BABA-treated cucumber plants. In addition, the infected cucumber with powdery mildew enhanced the expression levels from1.6 to 47.3 fold of the defense genes PAL, PR3, PR1, Lox1, Lox 23, LecRK6.1, WRKY20, and Cupi4 compared to heathy cucumber. The results suggests the BABA induced defense response is associated with SA signal pathway-dependent systemic acquired resistance (SAR) in cucumber, which is involved in plant resistance mechanisms.

Systemic lupus erythematosus (SLE) is a prevalent autoimmune condition, yet its alignment with a specific host immunological pathway remains unclear. The THαβ host immunological pathway, recognized for its defense against viruses and prions, has recently emerged as a response to DNA, RNA, and protein pathogens. SLE patients often produce anti-double strand DNA antibodies and anti-nuclear antibodies, suggesting a potential association with the THαβ host immune reaction. Throughout the course of SLE, elevated levels of type 1 interferons, type 3 interferons, interleukin-10, IgG1, and IgA1 are commonly observed. These cytokines and antibody isotypes are indicative of the THαβ host immunological pathway. Similarly, Myasthenia gravis, Grave’s disease, Graft versus host disease, autoimmune hemolytic anemia, immune thrombocytopenia, dermatomyositis, and Sjogren’s syndrome are also linked to THαβ-related type 2 hypersensitivities. Considering the potential association of these diseases with dysregulated THαβ immune responses, therapeutic strategies such as anti-interleukin-10 or anti-interferons α/β could be explored to manage these disorders effectively.

Weather radar plays a crucial role in the monitoring of severe convective weather. However, due to its limited detection range, it cannot conduct effective monitoring in remote offshore areas. Therefore, this paper utilized the Unet++ to establish a model for retrieving radar composite reflectivity based on Himawari-9 satellite datasets. In the process of comparative analysis, we found that both satellite and radar data exhibit significant diurnal cycles, but there are notable differences in their variation characteristics. To address this, we established 4 comparative models to test the influence of latitude and diurnal cycles on the inversion results. The results show that adding the distribution map of the minimum cloud-top brightness temperature at the corresponding time in the model can effectively reduce the root mean square error (RMSE) of the model and enhance the accuracy of severe convective weather monitoring. This conclusion can be applied in the future to enhance and improve satellite quantitative precipitation estimation.

Mechanochemical production of copper (II) isonicotinate Metal-Organic Framework ([Cu (INA)2]-MOF) and its modified magnetic iron composite ([Cu (INA)2]-MOF@Fe3O4]) allowed for the adsorptive removal of Terbutaline from water. A variety of characterization techniques, including Fourier, transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), X-ray diffraction (XRD), and scanning electron microscopy (SEM), were used to elucidate the distinct chemical and morphological features of the two advanced materials. The optimal adsorption conditions were determined by investigating a wide range of adsorption-related variables, including contact time, initial Terbutaline concentration, adsorbent dosages pH, and temperature. The chemistry involved in the adsorption process between the adsorbents and the adsorbate molecules was evaluated using the best-fitting models, such as kinetics, isotherms, and thermodynamics, and the regeneration study was performed to evaluate the adsorbents' reusability. Incredibly maximum adsorption capacities (Qmax) of 1667 and 2500 mg L-1 were attained within 40 minutes under alkaline pH 11 by the [Cu (INA)2]-MOF and the [Cu (INA)2]-MOF@Fe3O4, respectively. The adsorbents have been proven to be good for the adsorption of Terbutaline as a priority pollutant in an aqueous solution, with pseudo-first order and Langmuir as the best-fitting models for the kinetic and isotherm models respectively.

Recently, various relations and criteria have been presented to establish a proper relationship between control systems and control Global Positioning System (GPS)-intelligent buoy system. Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique. This study aims at developing a novel controller based on adaptive fuzzy neural network to optimally track the dynamically positioned vehicle on water with unavailable velocities and unidentified control parameters. In order to model the network with the proposed technique, uncertainties and the unwanted disturbances are studied in the neural network. The presented study aims at developing a neural controlling which applies the vectorial back-stepping technique to the surface ships, which have been dynamically positioned with undetermined disturbances and ambivalences. Moreover, the objective function is to minimize the output error for the neural network (NN) based on closed-loop system. The most important feature of the proposed model for the positioning buoys is its independence from comparative knowledge or information on the dynamics and the unwanted disturbances of ships. The numerical and obtained consequences demonstrate that the controller system can adjust the routes and the position of the buoys to the desired objective with relatively few position errors.

This study utilised an ensemble of pre-trained networks and digital mammograms to develop a supplementary diagnostic tool for radiologists. Digital mammograms and their associated information were collected from the department of radiology and pathology, Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and explored in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks based on PR-AUC, precision, and F1 score. The final ensemble model had a mean precision, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated a balanced performance across mammographic density. In conclusion, this study exhibited the good performance of the ensemble transfer learning on digital mammograms for the purpose of breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus, reducing their workloads.

This study aimed to determine the feasibility of the development of an over-the-counter (OTC) screening model using machine learning for breast cancer screening in the Asian women population. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia. Five screening models were developed based on machine learning methods; random forest, artificial neural network (ANN), support vector machine (SVM), elastic-net logistic regression and extreme gradient boosting (XGBoost). Features used for the development of the screening models were limited to information from the patients’ registration form. The model performance was assessed across the dense and non-dense groups. SVM had the best sensitivity while elastic-net logistic regression had the best specificity. In terms of precision, both random forest elastic-net logistic regression had the best performance, while, in terms of PR-AUC, XGBoost had the best performance. Additionally, SVM had a more balanced performance in terms of sensitivity and specificity across the mammographic density groups. The three most important features were age at examination, weight and number of children. In conclusion, OTC models developed from machine learning methods can improve the prognostic process of breast cancer in Asian women.

Over the centuries, honey is known for its superior usage in culinary, and for its rich nutrition and therapeutic values which are scientifically proven in the medical field. The chemical composition of honey varies depending on its botanical sources and environment. Therefore, the nutrition content in honey is highly likely to be affected by contaminants, such as heavy metals and pesticides. To ensure the quality of honey, parameters such as the heavy metal content should be within the safe range of total standard mineral and trace elements as defined by the International Food Standard (Codex Alimentarius), and pesticides should not be present at all. The high concentration of heavy metal and pesticides not only deteriorates the quality and quantity of honey, but also causes harm to the bee colony itself. In the agriculture sector, the excessive usage of pesticides and fertilizer negatively impacts the overall honey production process. Bees, a pollinating agent, bring the polluted nectar back to their beehives, eventually contaminating the honey and depreciating its value. Hence, this article will comprehensively review the activities that contribute to heavy metal and pesticide contamination, the interactions of bees as a pollinating agent, the impact of the pollutant to the colonies, and subsequently to the honey production.

Joining of aluminium alloys to steels has been extensively studied, especially in the automotive sector. However, aluminium alloys are known to be difficult to join with steels when methods involving fusion welding are used because of hot cracking problem. Hence, a high strength joint between these dissimilar metals would be of benefit especially in reducing the weight of products. In this work the torch brazing method was applied to join AR500 steel with AA7075 aluminium alloy using Al-Si-Zn base filler metal at various flame times. The effect of the brazing work on the intermetallic phase formation and the mechanical strength of the joints were investigated. In this work, the maximum shear load obtained was 6460 N and the presence of the intermetallic phases had reduced the shear strength of the brazed joints. However, the torch brazing process using Al-Si-Zn filler metal had successfully facilitated the joining of these dissimilar metals.

Pomegranate juice (PJ) has total antioxidant capacity (TAC) which is reportedly higher compared to other common beverages. This short study aimed to evaluate the TAC of commercial PJ and pomegranate fruit in terms of a newly described iron (III) reducing antioxidant capacity (iRAC) and to compare with ABTS free radical quenching activity. Commercial PJ, freeze-dried pomegranate, and oven dried-pomegranate were analyzed. The total phenols content (TPC) was also assessed by the Folin-Ciocalteu method. The calibration results for iRAC were comparable to ABTS and Folin-Ciocalteu methods in terms of linearity (R2 > 0.99), sensitivity and precision. The TAC for PJ expressed as trolox equivalent antioxidant capacity (TEAC) was 33.4 ± 0.5 mM with the iRAC method and 36.3 ± 2.1 mM using the ABTS method. For dried pomegranates, TAC was 89–110 mmol/100 g or 76.0 ± 4.3 mmol/100 g using iRAC and ABTS methods, respectively. Freeze-dried pomegranate had 15% higher TAC compared with oven-dried pomegranate. In conclusion, pomegranate has high TAC as evaluated by the iRAC and ABTS methods, though variations occur due to the type of cultivar, geographic origin, processing and other factors. The study is relevant for attempts to refine food composition data for pomegranate and other functional foods.

This paper proposes an optimization method for the scheduling of a multi-energy virtual power plant supply-demand balance in the power market environment of Jiangxi Province. The objective of this method is to enhance the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method comprehensively considers the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Additionally, real-time market price feedback is integrated to achieve accurate allocation of power supply and demand. Through a case study of a multi-energy virtual power plant in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply-demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%.

In recent years, exterior-rotor brushless DC motors have become increasingly popular in robotics applications due to their compact shape and high torque density. However, these motors were originally used for continuous operation in drones. For applications such as exoskeletons, prostheses, or legged robots, short bursts of high power are often required. Unfortunately, vendors do not typically provide data on the motors' performance under these conditions. This paper presents experimental data on the torque-speed relationship, efficiency, and thermal responses of one of the most widely used outrunner-type brushless motors across its full operating range, including high-power, short-duration operation. The results of this study can inform the selection and design of actuators for a range of robotics applications, particularly those that require high power output for brief periods of time.

During the last two decades, energy poverty has captured a growing attention of researchers and policymakers due to its strong association with economic poverty and poor economic performance. This study uses a broad set of macro level indicators and makes the first attempt to measure energy poverty and its impact on economic growth of Pakistan over the period 1990 to 2017. In particular, our energy poverty indicator considers four main dimensions of energy poverty, namely, energy services, clean energy, energy governance and energy affordability. Our main results show that though the overall energy poverty has reduced in Pakistan during the selected sample period, the country shows an increasing dependence on polluted energy supply in order to meet its growing demand of energy. In second stage of the investigation, we test the neoclassical growth theory where we incorporate energy poverty along with human capital as source of economic growth. Our cointegration results reveal a strong relationship between energy poverty and economic growth that is also dynamically stable in short run. These strong negative linkages between energy poverty with economic growth for the sample economy complement the previous literature on the subject.

This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.

With an increasing concern for global warming, there have been many attempts to reduce greenhouse gas emissions. About 30 % of total energy has been consumed by buildings and much attention has been paid to reducing building energy consumption. While there are many ways of reducing building energy consumption, accurate energy consumption prediction becomes more significant. As mechanical systems are the most energy-consuming components in the building, the present study developed the energy consumption prediction model for a direct-fired absorption chiller by using the ANN technique for the short term. The ANN model was optimized and validated with the actual data collected through a BAS. For the optimization, the numbers of input variables and neurons, and the data size of training were applied. By changing these parameters, the predictive performance was analyzed. In sum, the outcome of the present study can used to predict the energy consumption of the chiller as well as improve the efficiency of the energy management. The outcome of the present study can be used to develop a more accurate prediction model with a few datasets in that it can improve the efficiency of building energy management.

This study examines the effect of a corporate citizenship-oriented high performance work system (CC-oriented HPWS) and the interactive influence of CC-oriented HPWS and person-organization fit (PO fit) on creativity at the individual level. Our analysis of field data collected from 303 employees in two branch companies with a two-phase collection method provides empirical support for our theoretical model. The results indicate that high performance work system (HPWS) reflecting corporate citizenship (CC) correlates positively with individual creativity. In addition, the moderating effect of PO fit is significant in the relationship between CC-oriented HPWS and creativity at the individual level. On this basis, this paper discusses the theoretical and practical implications of these findings for future research.

Recently, the awareness of energy conservation, carbon emission reduction, and environmental protection in Taiwan has begun to gradually improve, and bicycles have become an important vehicle for residents. In 2009, Kaohsiung began promoting public bicycle sharing. The number of users in that year amounted to 140,000. By 2015, the accumulated number of users reached nine million. This study adopted the Theory of Planned Behavior (TPB) to explore the factors influencing the behavioral intentions of public bicycle users, including the three dimensions of attitude, subjective norm, and perceived behavioral control, and probed into the relationship between the factors influencing the behavioral intentions of public bicycle users in Kaohsiung City and the dimensions. This study used a questionnaire to collect data and SPSS & AMOS for statistical analysis. The empirical results demonstrated that the most prominent factors influencing the behavioral intentions of public bicycle users in Kaohsiung City included perceived behavioral control, attitude, and subjective norm, in order. Among the influencing factors in the three dimensions, personal interests, main groups, and self-competence had a positive and significant correlation. Administrators should provide more incentives related to the three influencing factors to encourage the public in Kaohsiung City to use public bicycles more frequently as their short-distance travel mode, so as to achieve the purposes of saving energy, reducing carbon emissions, and protecting the urban environment.

With continued technological innovation in the fields of mixed reality (MR), wearable-type MR devices, such as helmets, have been released and are frequently used in various fields, such as entertainment, training, and education. However, because each product has different parts and specifications in terms of the design and manufacturing process, users feel that the virtual objects overlaying real environments in MR are visualized differently depending on the scale and color used by the MR device. In this paper, we compare the effect of scale and color parameters on users’ perception in using different types of MR devices to improve MR experience. We conducted two experiments (scale and color), and our experimental study showed that the subjects who participated in the scale perception experiment clearly tended to underestimate virtual objects, compared with real objects, and overestimate color in MR environments. [MM1]Please confirm meaning.

In this work, we report on the first attempt using size-controlled ZnO nanoparticles (NPs) to analyze role of thioacetamide (TAA) in forming ZnS nanostructures from ZnO. The size-controlled B(blue)_, G(green)_, Y(yellow)_ZnO QDs, and NC (nanocrystalline)_ZnO NPs were synthesized by sol-gel process as well as hydrothermal method, respectively and then reacted with an ethanolic TAA solution as sulfur source. After reacting with TAA, the formation of ZnS QDs was observed over time. The chemical reactions were identified using X-ray diffraction (XRD), Transmission Electron Microscope (TEM), UV-vis spectroscopy, and Photoluminescence (PL). All results indicate that ZnS formation is due to direct crystal growth and/or chemical conversion of ZnO to ZnS. Our results enable a broader understanding of the synthetic mechanisms involved in the use of TAA as a sulfur source in forming ZnS nanostructures from ZnO.

In the present study, piecewise integrated composite (PIC) bumper beam for passenger cars was proposed and design optimisation process for composite bumper beam against IIHS test was carried out with the help of machine learning. Several elements in IIHS bumper FE model have been assigned to be references, in order to collect training data which, allow the machine learning model to study the method of predicting loading types of each finite element. 2-D and 3-D implementations were provided by machine learning models, which determined stacking sequences of each finite element in PIC bumper beam. It was found that the PIC bumper beam, which was designed by machine learning model has direct impact on reducing the possibility of failure as well as increasing bending strength effectively than conventional composite bumper beam. Moreover, 3-D implementation produced better results compared with 2-D implementation since it was preferable to choose loading type information which was achieved from surroundings when the target elements were located either at corner or junction of planes instead of using information came from the same plane of target.

Background: Little information exists regarding the differences in the clinical and laboratory characteristics of Kikuchi-Fujimoto disease (KFD) according to age. Objective: To evaluate the clinical and laboratory characteristics of KFD according to age. Methods: We retrospectively evaluated patients diagnosed with KFD at Pusan National University Hospital between 2010 and 2020. Results: Eighty patients (46 children and 34 adults) with a mean age of 21.5 ± 11.8 years (range, 3–49 years) were included in the study. Among children, the male sex ratio was higher, in adults, the female sex ratio was higher. Fever, tenderness in the lymph node, and skin rashes were more common in children, while myalgia and weight loss were more common in adults. In children, the recurrence rate was significantly higher among boys than among girls (15.8% vs 0.0%, P=0.001). EBV and ANA positivity rates were higher in boys than in girls. In adults, the recurrence rate was significantly higher in women than in men (18.2% vs 0.0%, P=0.005). ANA positivity rates were higher in women than in men. Conclusion: The clinical features, laboratory findings, and recurrence of KFD may differ depending on age and sex. Clinicians should be aware of this.

Bacillus Calmette-Guérin (BCG) is the only widely used prophylactic tuberculosis (TB) vaccine that can prevent severe TB in infants. However, it provides poor protection in adults, and therefore, there in ongoing research on new TB vaccines and immunization strategies with more durable immune effects. The recombinant BCG and BCG prime-protein booster are two important vaccine strategies that have recently been developed based on BCG and could improve immune responses. In this study, three immune strategies based on four protective antigens, namely, ESAT-6, CFP-10, nPPE18, and nPstS1, were applied to construct recombinant rBCG-EPCP009, EPCP009 subunit protein, and BCG prime-EPCP009 booster vaccine candidates. The short- and long-term immune effects after vaccination in Balb/c mice were evaluated based on humoral immunity, cellular immunity, and the ability of spleen cells to inhibit in vitro mycobacterial growth. At 8 and 12 weeks after the initial immunization, splenocytes from mice inoculated with the BCG prime-EPCP009 protein booster secreted higher levels of PPD- and EPCP009-specific IFN-γ, IL-2, TNF-α, IL-17, GM-CSF, and IL-12 and had a higher IFN-γ⁺CD4⁺ TEM:IL-2⁺CD8⁺ TCM cell ratio than splenocytes from mice inoculated with the rBCG-EPCP009 and EPCP009 proteins. In addition, the EPCPE009-specific IgG2a/IgG1 ratio was slightly higher in the BCG prime-EPCP009 protein booster group than in the other two groups. The in vitro mycobacterial inhibition assay showed that the splenocytes of mice from the BCG prime-EPCP009 protein booster group exhibited stronger inhibition of Mycobacterium tuberculosis （M. tuberculosis）growth than the splenocytes of mice from the other two groups. These results indicate that the BCG prime-EPCP009 protein booster exhibited superior immunogenicity and M. tuberculosis growth inhibition to the parental BCG, rBCG-EPCP009, and EPCP009 proteins under in vitro conditions. Thus, the BCG prime-EPCP009 protein booster may be important for the development of a more effective adult TB vaccine.

This paper examines interlinkages and hedging opportunities between nine major cryptocurrencies for the period between 30 September 2015 and 4 June 2020, which notably includes the coronavirus disease 2019 (COVID-19) outbreak lasting from early 2020 through the end of the sample period. The results of dynamic conditional correlation (DCC) analysis using a minimum connectedness approach show a high degree of correlation between cryptocurrencies throughout the sample period. However, the correlations reach their minimum values during the COVID-19 pandemic, which indicates that cryptocurrencies acted as a hedge or safe haven during the stressful period of the COVID-19 pandemic. The weight of cryptocurrencies was significantly reduced and their hedging effectiveness varied greatly during the pandemic, which indicates that investors’ preferences changed during the COVID-19 period.

Asthma is a chronic respiratory disease characterized by hyperreactivity in the airways, which can lead to significant lung damage if not properly managed. While placebos—medications with no active therapeutic ingredients—are sometimes used in clinical trials to gauge the effectiveness of asthma treatments, their long-term use raises concerns. Placebos work primarily through psychological mechanisms by enhancing patients’ expectations of treatment. However, prolonged reliance on placebo treatment in asthma could potentially mask symptoms, delay proper management, and lead to undetected lung damage or other adverse effects. This study explores the potential risks and consequences associated with long-term placebo use in asthma management.

With the increase of intelligent voice phishing and the increasing reliance on open banking systems, there has been a rise in cases where individuals' personal information has been exposed, resulting in significant financial losses for the victims. Non-face-to-face transactions in the financial sector face challenges such as customer identification, ensuring transaction integrity, and preventing transaction rejection. Blockchain-based distributed ledgers have been proposed as a solution, but their adoption is limited due to the difficulty of managing private keys and the burden of gas fees management. This paper proposes a non-face-to-face P2P real-time token payment system that minimizes the risk of key loss by storing private keys in a keystore file and database through a server-based key management module. The proposed system simplifies token creation and management through a server-based token management module and implements an automatic gas charging function for smooth token transactions. Transaction integrity and non-repudiation are ensured through a transaction confirmation module that uses transaction IDs without exposing personal information. Furthermore, advanced security measures such as blocking foreign IP access and DDoS defense are implemented to securely protect user data. The proposed system aims to provide a convenient, secure, and accessible online payment solution to the public by implementing a self-authentication function using a web application that is not limited to smart phones or application platforms.

We analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and that stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensify the spillover effects across international stock markets. Our results demonstrate that the EU, Ger-many, and the US acted as net transmitters of dynamic connectedness; however, Japan (JP), China (CH), and India (IN) acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants.

The weakness of the dispersion-managed link combined with optical phase conjugation to compensate for optical signal distortion caused by chromatic dispersion and nonlinear Kerr effect of standard single mode fiber is its limited structural flexibility. We propose dispersion map that can simultaneously compensate for the distorted wavelength division multiplexed signal while increasing the configurational flexibility. Each residual dispersion per span (RDPS) in the former half of the proposed link is randomly determined, and in the latter half, the arrangement order of RDPS is the same as or inverted in the former half. We confirm that the dispersion maps in which the RDPS distribution pattern in the latter half is opposite to the arrangement order in the former half are more effective in compensation, and rather, the compensation effect is better than in the dispersion map of the conventional scheme. The notable result of this paper is that the increase of flexibility can be achieved through random arrangement of RDPS in the former half, and the compensation improvement can be achieved by through inverse arrangement in the latter half which make the distribution profile of each half link roughly symmetric with respect to the midway optical phase conjugator.

Refinery planning optimization is a challenging problem as regards handling the nonconvex bilinearity mainly due to pooling operations in processes such as crude oil distillation and product blending. This work investigates the performance of several representative piecewise-linear (or piecewise-affine) relaxation schemes (referred to as McCormick, bm, nf5, nf6t, and de (which is a new approach proposed based on eigenvector decomposition) that mainly give rise to mixed-integer optimization programs to convexify a bilinear term using predetermined univariate partitioning for instances of uniform and non-uniform partition sizes. Computational results show that applying these schemes give improved relaxation tightness than only applying convex and concave envelopes as estimators. Uniform partition sizes typically perform better in terms of relaxation solution quality and convergence behavior. It is also seen that there is a limit on the number of partitions that contributes to relaxation tightness, which does not necessarily correspond to a larger number of partitions, while a direct relation between relaxation size and tightness does not always hold for non-uniform partition sizes.

A highly crystallized monoclinic-scheelite type BiVO4 powders were successfully synthesized by solvothermal method. The as-synthesized BiVO4 powders were characterized by XRD, FE-SEM, Raman spectroscopy, UV-vis DRS spectroscopy and TA-PL. From the XRD data and Raman spectra, the monoclinic-scheelite phase BiVO4 sample can be obtained at higher solvothermal synthesis temperature more than 140 oC. The preparation conditions such as, the Bi/V molar ratio and synthesis temperature, have significantly effects on the morphologies of the BiVO4 samples. BVO2 sample shows the highest PL peak, which has the highest formation rate of OH radicals and the highest photocatalytic activity. This result suggests that the formation rate of OH radicals shows a good correlation with the photocatalytic activity.

Quadruped robots possess significant mobility in complex and uneven terrains due to their outstanding stability and flexibility, making them highly suitable in rescue missions, environmental monitoring, and smart agriculture. With the increasing use of quadruped robots in more demanding scenarios, ensuring accurate and stable state estimation in complex environments has become particularly important. Existing state estimation algorithms relying on multi-sensor fusion, such as those using IMU, LiDAR, and visual data, often face challenges on non-stationary terrains due to issues like foot-end slippage or unstable contact, leading to significant state drift. To tackle this problem, this paper introduces a state estimation algorithm that integrates an Invariant Extended Kalman Filter (InEKF) with a disturbance observer, aiming at estimating the motion state of quadruped robots on non-stationary terrains. Firstly, foot-end slippage is modeled as a deviation in body velocity and explicitly included in the state equations, allowing for a more precise representation of how slippage affects the state. Secondly, the state update process integrates both foot-end velocity and position observations to improve the overall accuracy and comprehensiveness of the estimation. Lastly, a foot-end contact probability model, coupled with an adaptive covariance adjustment strategy, is employed to dynamically modulate the influence of the observations. These enhancements significantly improve the filter's robustness and the accuracy of state estimation in non-stationary terrain scenarios. Experiments conducted with the Jueying Mini quadruped robot on various non-stationary terrains show that the enhanced InEKF method offers notable advantages over traditional filters in compensating for foot-end slippage and adapting to different terrains.

Since the past few decades, numerous modifications and innovations have been done to design the optimal corneal biomatrix for corneal epithelial cells (CECs) or limbal epithelial stem cells (LESCs) carriers. However, researchers have yet to discover the ideal optimization strategies in the development of corneal biomatrix design and its effects on cultured CECs or LESCs. This review further discusses and summarizes recent optimization strategies to develop an ideal collagen biomatrix and its interaction with CECs and LESCs. Using PRISMA guidelines, the articles published from June 2012 to June 2022 were systematically searched using Web of Science (WoS), Scopus, PubMed, Wiley, and EBSCOhost databases. The literature search identified 444 potential relevant published articles, with 29 relevant articles selected based on the inclusion and exclusion criteria after the screening and appraising processes. The current paper highlights the physicochemical and biocompatibility (in vitro and in vivo) characterization methods, which were inconsistent throughout the different studies. Despite the variability in the methodology approach, the reviewer postulated that the modification of the collagen biomatrix improves its mechanical and biocompatibility properties toward CECs and LESCs. All findings were discussed in this review; thus, it provides a general view of up-to-date trends in this field.

The potential effect of implementing environmental regulations on economic growth is a controversial issue for a long time. As portrayed by Porter hypothesis, environmental regulations may impact on economic growth by reconstructing the economic structure. A number of previous literature proved the connections between the above three parties. With the intention of exploring the nature of this mechanism, it is important to understand the internal inter-relation of the three parties, requiring data from a large economy experiencing the transition in economic structure. By constructing a panel containing data from 30 Chinese provinces over 10 years (2008-2019), this paper investigate the dynamic inter-relation of the three parties by introducing PVAR model with Granger Test. Results reveal that environmental regulations give a “U” shaped impact to economic growth. In return, economic growth promotes the development of economy with a weakening strength. In addition, results also support the hypothesis that economic structure is the intermediate of economic growth and environmental regulations. There is a rooftop for the effect of economic structure to environmental regulations. The rooftop may signify the best optimization of primary structure.

Characters are one of the most important elements in composing digital animation. The appear-ance and voice of a character should be designed to express the personality and values of the character. However, it is not easy for animation producers to harmoniously match the appear-ance and voice of a character. Advances in deep learning technology have made it possible to overcome this limitation. To achieve this, firstly, an audio-visual dataset of characters is required. In this study, we construct and verify a Korean audio-visual dataset consisting of frontal face im-ages of various characters and short voice clips. We developed an application that can automati-cally extract the frontal face image and a short voice clip of a character by collecting videos up-loaded to YouTube. Through this, a dataset consisting of a total of 1,522 face images and a total of 7,999 seconds of voice clips was built based on 490 characters. Furthermore, we automatically la-bel characters by gender and age to validate the dataset. The dataset built in this study is expected to be used in various deep learning fields, such as classification, generative adversarial networks, and speech synthesis.

Based on the 2014-2015 China Education Panel Survey (CEPS) and using the propensity score matching method, we studied the causal relationship between physical exercise and prosocial behavior of junior middle school students in China. Ordinary least squares regression and propensity score matching estimation results showed that participation in physical exercise significantly increases students’ prosocial behavior by more than 0.2 standard points. The results of this study were tested and found to be robust.

A scientific investigation into visual preferences of rural recreation path landscapes is crucial for enhancing visual qualities and visitors' experience. Taking Baofeng, a representative traditional village in Yunlong, as an example, this study analyzed the visual preference of three typical linear recreational paths with different landscape attributes. We used eye-tracking technology to collect eye movement data and conducted a subjective semantic differential evaluation. Our findings manifest a general inclination amongst individuals to favor closer views and paths with clear trajectories and rich elevation changes while walking, despite preference differences on different types of paths. Regarding group diversity, more inclusive design should be made to meet the sensory needs of different groups. Moreover, despite the limited level of correlation between subjective and objective evaluation methods, combining these two approaches remains indispensable. This synthesis advances prior quantitative landscape evaluation with a specific focus on rural path landscapes and upholds the integrity of results by combining objectivity with subjectivity. It also provides relevant design suggestions for further rural renewal and path planning of Baofeng village.

Immunosensors have been widely developed to use antibodies to detect a pathogen of interest; it is interesting to look at the effect of nonspecific antibody binding to E. coli using electrochemical methods. IgG antibody not specific to E. coli O157:H7 was crosslinked onto a screen-printed carbon electrode. The presence of E. coli at 4, 4 × 10², 4 × 10⁵, and 4 × 10⁸ CFU/mL on the electrode surface was detected via linear sweep voltammetry (LSV) and electrochemical impedance spectroscopy (EIS). Current transfer at both electrodes was reduced as the concentration of bacteria increased; however, the calibration of number of cells to decreased current was nonlinear for IgG-modified electrodes. The nonlinearity is confirmed by EIS measurements which showed highest impedance at 4 CFU/mL E. coli when impedance should be the lowest. FESEM images showed higher binding of cells when IgG is present compared to electrodes with reduced graphene oxide (rGO) alone. Electrodes with rGO alone show less attachment of E. coli, with EIS showing a linear calibration profile, while LSV shows not much difference in current values for all concentrations aside from the highest concentration. These results suggest that nonspecific binding can provide false signals in electrochemical measurements, and it is crucial to provide proper controls.

The lack of a generalizable machine learning model for predicting the safety of food for 1 human consumption is a significant challenge for policymakers and responsible authorities. This 2 study provides a step-by-step guide to predict the results of seafood product import inspections, 3 focusing on identifying and understanding the critical factors that influence these results. By compar- 4 ing the performances of an ensemble of machine learning models, this study combines the strengths 5 of multiple algorithms to improve the predictive accuracy and gain insights into the key factors 6 impacting them. The ensemble model based on the soft voting technique achieves superior perfor- 7 mance to that based on the hard voting technique in terms of the recall and area under the curve 8 (AUC) scores. The study discovered that various characteristics, such as the exporting country ratio, 9 major product category, overseas manufacturer ratio, importer ratio, and seasonal variation, had a 10 substantial influence on the models’ decisions. This research guide for predicting seafood product 11 import inspection results could pave the path for other items to follow.

Power grid fault diagnosis technology has been widely used at home and abroad. With the access of distributed power sources and diversified loads, the distribution network has changed from a passive network to an active network, and the current has changed from "one-way" to "two-way". The structure of distribution network, equipment environment and operating conditions are becoming more and more complex, and the fault diagnosis and localization of active distribution network is becoming more and more difficult. With the rapid development of artificial intelligence, power grid fault diagnosis based on intelligence is unprecedented. This paper introduces the research status of distributed photovoltaic grid-connected fault diagnosis technology, and expounds the history of fault diagnosis technology, which mainly includes expert system, artificial neural network, Bayesian network, fuzzy set theory, rough set theory, Petri net analytical model and multi-source information fusion, etc. The applicability and characteristics of these diagnoses are described, and the existing defects and the overall direction of improvement are briefly described Finally, the key technical problems and future directions in the field of power system fault diagnosis are pointed out by combining theory with practice, so as to promote the further development of this field.

Structural magnetic resonance imaging (sMRI) is widely used in the clinical diagnosis of diseases due to its advantages: high definition and noninvasive. Therefore, computer-aided diagnosis based on sMRI images is broadly applied in classifying Alzheimer’s disease (AD). Due to the excellent performance of Transformer in computer vision, Vision Transformer (ViT) has been employed for AD classification in recent years. ViT relies on access to large datasets, while the sample size of brain imaging datasets is relatively insufficient. Moreover, the pre-processing procedures of brain sMRI images are complex and labor-intensive. To overcome the limitations mentioned above, we propose Resizer Swin Transformer (RST), a deep learning model that can extract information from brain sMRI images that are only briefly processed to achieve multi-scale and cross-channel features. In addition, we pre-trained our RST on a natural image dataset and obtained better performance. The experimental results of ADNI and AIBL datasets prove that RST can achieve better classification performance in AD prediction compared with CNN-based and Transformer models.

The widespread integration and growing dependence among currency, stock and commodity markets make these markets often very vulnerable to shocks and at risk of collapse at the same time. As a result, these trends threaten the sustainability of the entire financial system. In this study, we explore the spillovers and nonlinear dependences between the seven major foreign ex-change rates, crude oil and gold prices, a global stock price index, and oil and stock implied volatility indices as proxy variables for global risk factors by em-ploying directional spillover network approach. We also use multi-scale de-composition method and nonlinear causality test between these variables to capture multi-level relationships at short and long horizons. Major findings are summarized as follows. First, from the multi-scale decomposition analysis, we identify that Granger causality test results and the direction and strength of return spillovers change with the level of decomposition. Second, the results of nonlinear causality tests show variation in both the significance and direction of Granger causality relationships between the decomposed currency and other series at different timescales, especially for the decomposed oil, gold, and OVX series. Third, the measured directional spillover indices identify the EUR as the largest contributor of connectedness to the other series. The central role of the EUR is a net transmitter of connectedness to gold, oil, the GBP, JPY, and CHF.

Recently, two respiratory syncytial virus (RSV) vaccines based on the prefusion F (pre-F) antigen were approved in the United States. We developed and validated an enzyme-linked immunosorbent assay (ELISA)-based protocol for the practical and large-scale evaluation of RSV vaccines. Two modified pre-F proteins (DS-Cav1 and SC-TM) were produced by genetic recombination and replication using an adenoviral vector. The protocol was established by optimizing the concentrations of the coating antigen (pre-F proteins), secondary antibodies, and choice of blocking buffer. To validate the protocol, we examined its accuracy, precision, and specificity using serum samples from 150 participants across various age groups and the standard serum provided by the National Institute of Health. In the linear correlation analysis, coating concentrations of 5 and 2.5 μg/mL of DS-Cav1 and SC-TM, respectively, showed high coefficients of determination (r2 = 0.90). Concentrations of secondary antibodies (alkaline phosphatase-conjugated anti-human immunoglobulin G, diluted 1:2000) and blocking reagents (5% skim milk/PBS-T) were optimized to minimize non-specific reactions. High accuracy was observed for DS-Cav1 (r2 = 0.90) and SC-TM (r2 = 0.86). Further, both antigens showed high precision (coefficient of variation &lt; 15%). Inhibition ELISA revealed cross-reactivity of antibodies against DS-Cav1 and SC-TM but not with the attachment protein.

Tobacco smoke is an environmental mixture including polycyclic aromatic hydrocarbons and may interfere in endocrine system as an EDC in aging. Thus, we performed a molecular epidemiological approach with in-depth biological monitoring of combustible cigarette smoking among adolescents and adults in South Korea (N=620) with exposure biomarkers, i.e., exhaled CO, urinary cotinine, t,t-muconic acid (TTMA), malondialdehyde (MDA), and obtained information of their lifestyle and tobacco addiction status. We also diagnosed the genetic polymorphisms on the 96 SNPs for tobacco-metabolism, -addiction, and expression differences and compared mtDNA abnormalities in buccal and blood cells. As results, there were positive associations among the above tobacco exposure biomarkers. Man or youth smokers showed high frequency in some of mtDNA alteration, such as ‘SNPs for inconsistent bases between buccal and blood cells’. Among the SNPs, the polymorphisms on SULT1A1, DRD2, and ADH1B were related to multi-exposure biomarkers. Interestingly, youth showed similar levels of urinary TTMA and MDA to adults, although their pack-year was approx. 1/6 volume of that of adults. We also observed the negative association between urinary MDA levels and the growth rate in the adolescents (p<0.05). In conclusion, the present biological monitoring provides high susceptible population, i.e., adolescents rather than adults, and reliable genetic factors affecting tobacco exposure. The inferred environment-gene-gene interaction suggests tobacco smoking accelerats aging in adolescents as an EDC.

In this study, the microbial community during the barley yeast fermentation of stevia leaves and its correlation with antioxidant, antiobesity, and anti-inflammatory properties, as well as metabolites, were investigated using UPLC-Q-TOF-MS. Stevia was fermented using commercial TSB media (TSB 1, TSB 2, and TSB 3) and sucrose (Sucrose 1, Sucrose 3, and Sucrose 3) for 1, 3, and 5 days, respectively. Stevia ferments showed higher DPPH and ABTS radical scavenging capacity compared to samples incubated with sucrose for 5 days, and all six ferments inhibited nitric oxide production in a concentration-dependent manner in LPS-induced mouse macrophages. Furthermore, UPLC-QTOF-MS analysis identified 23 related substances, including 10 terpenoids, including rubusoside, steviolbioside, and rebadioside derivatives; dulcoside A; and phlomisoside II, which are indicators of stevia, as well as five flavonoids, four phenolic acids, and four fatty acids. We also identified the microbial community during fermentation via the next-generation sequencing of the 16S rRNA gene for bacteria and the internal transcribed spacer (ITS) gene for fungi. The results showed that TSB 1 and Sucrose 1 ferments were dominated by the pathogens Enterococcus hirae (58.93%) and Cronobacter sakazakii (80.92%), while samples fermented for more than 3 days were pathogen-free and dominated by lactic acid bacteria such as Pediococcus stilesii (73.37%). Microbial community analysis using the ITS region showed that Saccharomycopsis fibuligera, classified as a yeast rather than a mold, dominated the stevia fermentation regardless of the fermentation duration. In particular, the microbial community of the fermentation with a sugar solution was dominated by S. fibuligera by more than 99% throughout the fermentation periods of 1, 3, and 5 days. Finally, to apply the stevia ferments topically to human skin, skin irritation tests were performed on 30 volunteers. The results showed that the highly concentrated extracts (1 mg/mL) of all six stevia ferments were hypoallergenic. Taken together, these findings suggest that brewer's yeast fermented from stevia leaves with a sugar solution offers promise as a natural ingredient for use in functional foods and cosmetics.

In order to assess the range of impact of thermal wastewater discharge from the third Nuclear Power Plant (NP_No.3) on the local ecosystem, a three-dimensional ocean model (MITgcm) driven by tidal and climatological forcings is employed to study the present work. Tides and daily wind forcings are incorporated into the MITgcm model to examine their effects on the thermal plume and the water circulation in Nan Wan Bay. The model results reveal that the thermal plume is most likely to disperse to the southwest in summer; it is unlikely to drift to the southeast or northeast because of the presence of gentle southwesterly monsoon. In winter, the thermal plume is most likely to be directed to the southwest but barely visible in the northeast or southeast directions because of prevailing northeasterly monsoon. Additionally, it is worth emphasizing that strong tidal currents generate a pair of counter-rotating eddies that significantly dominate the dispersal of the thermal plume. However, seasonal monsoons also play an essential role in modifying the plume’s direction and dispersion. The model results reveal that the flow field and thermal plume agree well with observational data.

This comprehensive review explores the advancements in Orthognathic and Oral Maxillofacial Surgery, focusing on the integration of 3D Printing and Virtual Surgical Planning (VSP). Traditional surgical methods, while effective, come with inherent risks and complications, and can lead to variability in outcomes due to the reliance on the surgeon's skill and experience. The shift towards patient-centric care necessitates personalized surgical methods, which can be achieved through advanced technology. The amalgamation of 3D printing and VSP revolutionizes surgical planning and implementation by providing tactile 3D models for visualization and planning, and accurately designed surgical guides for execution. This convergence of digital planning and physical modeling facilitates a more predictable, personalized, and precise surgical process. However, the adoption of these technologies presents challenges, including the need for extensive software training and the steep learning curve associated with computer-aided design programs. Despite these challenges, the integration of 3D printing and VSP paves the way for advanced patient care in orthognathic and oral maxillofacial surgery.

Metabolic-associated fatty liver disease (MAFLD) and diabetic kidney disease (DKD) share various pathophysiological factors, and epidemiological evidence suggests that these two diseases are associated. Albuminuria and the estimated glomerular filtration rate, which are conventional biomarkers of DKD, are reportedly associated with the risk or severity of MAFLD. Recently, novel DKD biomarkers reflecting renal tubular injury have been introduced to complement conventional DKD markers. In this article, we looked at previous studies that showed an association between MAFLD and DKD and also reviewed the significance of DKD biomarkers as predictive risk factors for MAFLD.

To present surgical methods and outcomes of neobladder-vaginal fistula (NVF) repair after radical cystectomy (RC) with ileal orthotopic neobladder (IONB). Methods: We retrospectively reviewed 136 women who underwent RC with IONB for bladder cancer between January 2010 and December 2018. The NVF was confirmed by cystoscopy and/or voiding cystography. NVF repair was performed using a transvaginal approach, which included circumferential incision of the fistula tract, creation of a plane between the neobladder serosa and the vaginal epithelium, and multi-layered transvaginal closure. Results: During a median follow-up of 47.9 months, NVF was identified in 12 (8.8%) women. Eight fistulas were located in the proximal anterior vaginal wall and four in the vaginal apex. Median time from RC to NVF repair was 3.4 months. Median NVF size and duration of urethral Foley catheter indwelling was 6.0 mm and 24.0 days, respectively. Initial repair of NVF was successful in ten (83.3%) patients. Two (16.7%) patients who relapsed retained IONB through the subsequent operation. Two (16.7%) patients developed urinary incontinence after NVF repair, requiring anti-incontinence surgery. Conclusions: The transvaginal approach for NVF repair is feasible, yielding successful surgical outcomes. However, women should be counseled about the risks of relapse and urinary incontinence.

Background: In long-term care facilities, there are many residents who do not have the ability to seek shelter by themselves in the case of an emergency. Thus, it is extremely important that the staff of nursing homes are equipped with correct disaster prevention concepts, emergency survival responses, and hazard mitigation measures. Purpose: Discuss the intervention effectiveness of different fire prevention and emergency response trainings at nursing homes and the relationship and predictivity of awareness to self-efficacy. Method: Recruit staff from two nursing homes through purposive sampling, using a two-team pre-and post-test design to collect results from 41 individuals in the experiment group and 40 individuals in the control group. The research tool is the “Nursing Home Fire Prevention and Emergency Response Awareness and Self-Efficacy Scale,” to compare the effectiveness of advanced and general fire safety trainings. Result: After receiving improved advanced fire safety training, the total score and the result of the experiment group on fire prevention and emergency response awareness and self-efficacy had both performed better than the control group who received general fire safety training (p < .001); fire prevention and emergency response awareness has significant and positive correlation with self-efficacy (r=.601, p < .001), and awareness is a significant predictor variable to self-efficacy (p < .001). Conclusion/Practical Application: This study finds that the key to improving learning effectiveness includes adding fire science concept chapter when creating fire safety training material in order to strengthen basic awareness; fire safety training should comprehensively introduce all related duty responsibilities of staff fire defense formation, in turn enabling mutual responsive support for the needs of the site; also, to become familiarized with the knowledge requires appropriate frequency of training and enhancing the staff’s awareness to fire prevention and emergency response, which is the most important key of learning effectiveness.

We fabricated the nano-aperture plasmonic platforms for single molecule detection and other various applications such as infrared thermal emission device. The nano-apertures including the nanopores on the pyramid, and the nano-slits on the Au flat membrane were fabricated using a Ga ion focused ion beam drilling technique, followed by high energy electron beam irradiations dependent upon the electron beam current density. The nanopores with a few nanometer size and the nanoslit array with order of ~ 100 nm width were fabricated. Optical characteristics for the various nanoslits were examined dependent upon the slit opening width and sample thickness. The broad emission spectra from the (7x 7) slit array are obtained from spp-mediated emission in the visible and infrared region. A sharp strong infrared emission peak is also obtained due to Au nanoparticle. The fabricated Au platform can be utilized as single molecule sensor and infrared thermal emission device.

Background: We conducted research on the newly developed personalized recommendation service (PRS) of the global network of Korean scientists and engineers (KOSEN) in order to explore the information usage behavior and importance of the PRS used by Korean scientists and engineers. Methods: In order to understand information usage behavior, we gathered data from 513 survey results and analyzed them in terms of information usage behavior and the corresponding importance in each of the service quality areas. Results: We analyzed the 321 outcomes that indicated non-use of the PRS in order to understand the underlying reason(s); we employed 192 results that demonstrated the use of functionality to examine information usage behavior and importance. They found that the predominant motive for non-use of the service resulted from the respondents not knowing how to use it. According to demographic characteristics, the usage behavior of the PRS showed a difference regarding the purpose of using the service in the categories of gender and major field of study. Furthermore, users were concerned with various components of the PRS such as ease of use, design, relevance of content, user support, and interactivity. Conclusions: We suggest reinforcing user education degree and promotion to enhance the PRS. Since users were concerned with ease of use, design, relevance, user support, and interactivity, we recommend these as major points for improvement.

Despite the available literature on traumatic brain injury (TBI) biomarkers elsewhere, data are limited or non-existent in sub-Saharan Africa (SSA). The aim of the study was to analyse associations in acute TBI between admission serum biomarker concentrations and TBI severity, CT-scan findings and outcome as well as explore the influence of concurrent Toxoplasma gondii infection. Concentrations of serum biomarkers (GFAP, NFL Tau, UCH-L1, and S100B) were measured and Toxoplasma gondii detected in samples obtained <24 hours post-injury. The GOSE was used to evaluate 6 months outcome. All biomarkers levels increased with severity of TBI, but this increase was significant only for NFL (P=0.01). GFAP values significantly increased (P=0.026) in those with unfavorable outcome. Tau levels were higher in those who died (P=0.017). GFAP and NFL were sensitive to CT-scan pathology (p values respectively 0.004 and 0.002). S100B levels were higher (P<0.001) in TBI patients seropositive to Toxoplasma gondii. NFL was found to be sensitive to TBI severity while NFL and GFAP were predictive of CT intracranial abnormalities. Increased levels of GFAP and Tau were associated with poorer outcomes 6 months after TBI. S100B levels were significantly higher in T. gondii seropositive TBI patients.

We investigate the impact of air quality and weather on the equity returns of the Shenzhen Exchange. To capture the air quality and weather effects, we use dummy variables created by employing a moving average and moving standard deviation. The important results are as follows. First, in the whole sample period (2005–2019), we find that high air pollution and extremely high temperature have significant and negative influence on the equity returns. In the sub-period I (2005–2012), the 11-day model and 31-day model show that high air pollution have significant and negative impacts on the Shenzhen stock returns. Second, the results of the quantile regression show that high air pollution have significant and negative effects during bullish market phase, and extremely high temperature have significant and negative effects during bearish market phase. This implies that the air quality and weather effects are asymmetric. Third, the weather effect of the abnormal temperature on the stock returns is greater in severe bearish market. Whereas the effect of the air pollution on the stock returns is greater in the bullish market. Fourth, the least squares method underestimates the air quality and weather effects compared to the quantile regression method, suggesting that the quantile regression method is more suitable in analyzing these effects in a very volatile emerging market such as the Shenzhen stock market.

We investigate the impact of air quality and weather on the stock market returns of the Shenzhen Exchange. To capture the air quality and weather effects, we apply dummy variables generated by applying a moving average and moving standard deviation. Our study provides several interesting results. First, in the whole sample period (2005–2019), we find that high air pollution and extremely high temperature have significant and negative effects on the Shenzhen stock returns. In the sub-period I (2005–2012), the 11-day model and 31-day model show that high air pollution have significant and negative effects on the Shenzhen stock returns. Second, the results of the quantile regression show that high air pollution have significant and negative effects during bullish market phase, and extremely high temperature have significant and negative effects during bearish market phase. This implies that the air quality and weather effects are asymmetric. Third, the more the Shenzhen stock returns drop, the greater the effect of the abnormal temperature is. Whereas, the more the Shenzhen stock returns increase, the greater the effect of the abnormal air quality is. Fourth, the least squares method underestimates the air quality and weather effects compared to the quantile regression method, suggesting that the quantile regression method is more suitable in analysing these effects in a very volatile emerging market such as the Shenzhen stock market.

Granulomatosis with polyangiitis (GPA) is an autoimmune disease which is a type of anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis that frequently affects the lungs and kidneys. However, GPA limited to a single organ has also been reported. A 73-year-old man was admitted for back pain and fever. We detected elevated levels of inflammatory markers and myeloperoxidase-ANCA. Magnetic resonance imaging indicated diffuse inflammation of the back and psoas muscles. Histology showed degenerated muscle fibers and granulomatosis vasculitis with mixed lymphoplasma cell infiltration. High-dose methylprednisolone therapy improved his symptoms. A final diagnosis of GPA limited to the muscles was made.

Keywords: Kenaf; liquid cultivation; solid cultivation; in vitro propagation, life on land

This case report describes evidence of intratubular biomineralization in root canal filled with calcium enriched material after 8 years of clinical maintenance. The schematic findings of dentinal tubules were investigated with scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). The root canal obturation material was closely adapted to root dentin surface, suggesting the possibility of chemical bonding between the two interfaces. SEM and EDS observation of dentinal tubules showed intratubular biomineralized crystal structures with Ca/P ratio in a range of 1.30–2.12, suggesting bioactive capacity of calcium enriched cement.

In this study, the dependence between Bitcoin (BTC) and economic policy uncertainty (EPU) of USA and China is estimated by applying latest methodology of quantile cross-spectral dependence. The findings indicate a positive return interdependence between BTC and EPU is high in short-term, and this dependence decreases as investment horizons increase from weekly to yearly. The information on above interdependence is also extracted by applying wavelet coherence analysis and the estimation results suggest that correlation between BTC and EPU is positive during short-term investment horizon. Furthermore, more diversification benefits of BTC can be obtained during USA-EPU as compared to China-EPU.

It is difficult to effectively operate a dual-arm robot using only the information written in a cooking recipe. To cope with this problem, this paper proposes a graph based approach on bimanual cooking motion planning from a cooking recipe. In our approach, we first decompose the cooking recipe into graph elements. Then, we try to connect the graph elements taking into account the attributes of the input/output nodes. If two graph elements cannot be connected each other, we search for a graph element that can be inserted between them from the database of graph elements. Since the constructed graph includes the whole sequence of the robot’s motions to perform the cooking task, we can generate a task sequence of a dual-arm manipulator simultaneously performing two different tasks by using two arms. Through experimental study, we show that it is possible to generate robot motions from a cooking recipe and perform the cooking motions while simultaneously moving the left and right arms.

The study of man-land relationship in urbanization process is the current frontier and focus of international research. How to balance urban development and ecosystem conservation in the Upper Yellow River is a key issue for sustainable development in China. In this study, we evaluated the Lanzhou-Xining urban agglomeration （LXUA）by constructing a multi-dimensional assessment system for urbanization quality and ecosystem services. The efficacy function model, entropy weight method, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used to quantitatively assess the subsystems' state of development. Then, the coupling model (CD) and the coordination degree（CCD）model were used to explore the coupling coordination relationship and spatial-temporal change characteristics of the composite system. The findings indicate that: 1）In 2020, the quality of urbanization in LXUA showed the pattern of "double core". The development of urban centers in each city is insufficient, and the proportion of counties with low-level is too high. 2）Integrated ecosystem services showed an increasing distribution pattern from the northeast to the southwest. Water provision services, soil conservation services and carbon fixation services all showed growth trends. 3）Each county’s composite system was in the run-in stage or highly coupled stage. The subsystems were closely related to each other. 4）The CCD was decreased by 6% between two decades. The number of counties on the verge of disorder was the highest. About 80% of the counties are relatively lagging behind in ecosystem services.

ABSTRACT Introduction: Traditional penile venous surgery and enhancement have been controversial. Based on De Novo penile fibrovascular assembly, we report on penile venous stripping (PVS) and factual penile girth enhancement (FPGE). Methods: From 2013 to 2023, 18 men underwent PVS and FPGE, all confirmed with veno-occlusive dysfunction (VOD). PVS involves the stripping of erection-related veins. FPGE was performed along the tunica albuginea bilaterally; then, the two tunic defects were repaired with a 70.0x30.0 mm venous stripe and covered with either autologous venous walls (AVW, n=10) or Surgiform (SF, n=8). The penile girth was measured, and radio-opaque contrast was used. The abridged 5-item version of the International Index of Erectile Function (IIEF-5) score and the Erection Hardness Scale (EHS) were used yearly during follow-up via the Internet. Results: The follow-up period was 5.3±1.5 years. Radiopacity was consistently enhanced. Although there was indifference between AVW and SF, there was a significant improvement (both P＜0.01) in IIEF-5 and EHS scores (9.7±2.8 vs. 20.8±2.3; 1.7±0.6 vs. 3.2±0.2, respectively). Both the diameters of the glans and penile shaft increased (28.0±2.3mm and 28.3±2.1mm vs 35.3±2.2mm and 36.3±2.1mm respectively). The satisfaction rate was 81.3% (13/16). Conclusions: This novel combination of PVS and FPGE is promising, although a larger sample size is needed for further validation.

Crack inspection is important to monitor the structural health of pavement structures and to facilitate an easier rehabilitation process. Currently, pavement crack inspection is conducted manually, which is inefficient and costly at the same time. For solving the problem, this work has developed a robotic system for automated data collection and analysis in real-time. The robotic system navigates on the pavement and collects visual images from the surface. A deep learning-based semantic segmentation framework named RCDNet was proposed and implemented on the onboard computer of the robot to identify cracks from the visual images. Simulation results show that the deep learning model obtained 96.29% accuracy for predicting the images. The proposed robotic system was tested on both indoor and outdoor environments and was observed that it can complete inspecting a 3m × 2m grid within 10 minutes and a 2.5m × 1m grid within 6 minutes. This outcome shows that the proposed robotic method can drastically reduce the time of manual inspection. Furthermore, a severity map based on the results from visual images was also generated to provide an idea of which locations should be paid more attention to repair in a test grid.

This study employs the FLACS code to analyze hydrogen leakage, vapor dispersion, and subsequent explosions. Utilizing pseudo-source models, a liquid pool model, and a hybrid model combining both, we investigate dispersion processes for varying leak mass flow rates (0.225 kg/s and 0.73 kg/s) in a large open space. We also evaluate explosion hazards based on overpressure and impulse effects on humans. The computational results, compared with experimental data, demonstrated reasonable hydrogen vapor cloud concentration predictions, especially aligned with the wind direction. For higher mass flow rate of 0.73 kg/s, the pseudo-source model and hybrid model proved appropriate, while the liquid pool model was more suitable for lower mass flow rate of 0.225 kg/s. Regarding explosion analyses using overpressure-impulse diagram, higher mass flow rates leaded to potentially fatal overpressure and impulse effects on humans. However, lower mass flow rates may cause severe eardrum damage at the maximum overpressure point.

The aim of this study is to investigate transient dynamic flow characteristics of the fluid flow through 5-way electric coolant valve (PCCV : Penta-Control Coolant Valve). To achieve this goal, the present paper attempts the three-dimensional dynamic simulation of the fluid flow through the valve using a commercial CFD solver with moving mesh technique to consider flow inertia and dynamic flow in the opening and closing stages of the ball valve rotating motion. Firstly, dynamic flow characteristics and the thermal mixing inside the PCCV ball valve during the opening and closing stage was analyzed. It is found that the discrepancies between dynamic and steady state simulation is remarkable when fluxes with different levels of enthalpy and momentum flow into the PCCV leads to strong flow interference and flow inertia, while the discrepancies are relatively small at low rotation speed and weak flow interference. Then, the effect of dynamic flow characteristics of the valve on dynamic thermal mixing characteristics at two different ball valve rotational speeds and rotational directions were investigated. It was found that the dynamic flow and thermal mixing characteristics inside the PCCV are greatly affected by the rotation speed, rotation direction, and degree of flow interference between fluxes. It is helpful for the design of the better coolant control strategies and the improvement of the FCEV thermal management system.

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. Design rights specify the unique aesthetic characteristics of a product. Due to the rapid change of trends, new design rights are continuously generated. This work proposes an Ensemble Neural Network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. At first, we performed image classification and objection detection learning per design right using the existing models with a proven track record of high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiples the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was done an order of magnitude faster than the single model. ENN maintained a high level of accuracy as it scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as the customs at the border that deal with a swarm of products.

In this paper, we describe the surface modification of Polytetrafluoroethylene (PTFE) through plasma treatment process. Several parameters including different active gases; RF power; dis-tance between the plasma source and sample; and plasma duration were optimized to reduce the hydrophobic nature of PTFE. Three different active gases were used [i.e., N2, O2 and (Ar+H2)], in which N2 was effective to reduce the hydrophobicity of PTFE within shorter plasma duration of 2 minutes. Several surface characterizations including ATR-FTIR, water contact angle, FE-SEM and XPS were utilized to verify the neat and modified PTFE surface after plasma treatment. The plasma treatment using N2 as an active gas improved the wettability of PTFE membrane, show-ing a water contact angle of 108° when compared with the neat PTFE (140°). The SEM images of plasma treated PTFE shows greater modifications on the surface indicating non-uniform fiber alignment and torn fibers at several places. The obtained results confirm the fact that plasma treatment is an effective way to modify the PTFE surface without altering its bulk property.